CN116822685A - Multi-target site selection method and system for charging station - Google Patents
Multi-target site selection method and system for charging station Download PDFInfo
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Abstract
The application belongs to the technical field of charging stations, and particularly relates to a multi-target site selection method and system for a charging station. The method comprises the following steps: predicting the charging requirement of the electric automobile in a target area; initially determining candidate positions of the charging stations; and establishing a multi-target charging station site selection model by combining the charging requirements of the electric vehicles in the target area and the candidate positions of the charging stations, and carrying out simulation on the multi-target charging station site selection model to obtain the positions of all the candidate points of the charging piles. The system comprises a charging demand prediction module, a charging station candidate position selection module and a charging station site selection module. According to the application, the charging station planning social total cost optimal model is constructed based on factors such as the development scale and travel distribution of the electric automobile of the user, so that the planning level of the charging pile of the electric automobile can be improved, the comprehensive cost of planning and selecting the site of the charging pile can be reduced, and the waiting time of the user and the construction investment cost of the charging station can be reduced under the condition of meeting the charging requirement of the user. The system is convenient to calculate, and the planning speed of the charging pile of the electric automobile can be rapidly improved.
Description
Technical Field
The application belongs to the technical field of charging stations, and particularly relates to a multi-target site selection method and system for a charging station.
Background
The construction of the charging station is not only influenced by natural factors such as geological conditions, climate conditions and the like, but also limited by social factors such as funds, road network planning, power grid planning, consumption of electric vehicles, battery technology and the like. At present, china is in the primary stage of charging station construction, and due to various constraints such as funds, technology and the like, the electric vehicle charging station is preferably subjected to a staged batch dynamic construction process, and the charging station construction process is suitable for the popularization degree of the electric vehicle. The existing research results mainly construct a single-target static site selection model, and most of the results imply the assumption that funds are sufficient, the land is sufficient, no established charging facilities exist in the area, and the like, which obviously does not accord with the actual situation of the current charging station construction in China. In China, an electric vehicle charging station with a certain scale is built, the built charging station is taken as a constraint condition in the next charging station construction process, and meanwhile, factors such as land conditions, safety regulations and the like faced by the charging station are comprehensively considered.
Disclosure of Invention
Aiming at the technical problem that the currently built charging station has constraint on the address selection of the subsequent charging station, the application provides a multi-target address selection method and system of the charging station, and the specific technical scheme is as follows:
a multi-target site selection method of a charging station comprises the following steps:
step S1, predicting the charging requirement of an electric automobile in a target area;
step S2, a candidate position of a charging station is preliminarily determined;
and S3, establishing a multi-target charging station site selection model by combining the charging requirements of the electric vehicle in the target area and the candidate positions of the charging stations, and carrying out simulation on the multi-target charging station site selection model by using a queuing theory to acquire the positions of each candidate point of the charging piles.
Preferably, the step S1 specifically includes: dividing traffic locations according to travel rules of the target areas, predicting development scale and travel distribution of electric vehicles in each traffic location, and accordingly predicting charging requirements of the electric vehicles in each traffic location, and further obtaining charging requirements of the electric vehicles of the target areas; and the electric automobile charging requirement of the target area is the sum of the electric automobile charging requirements in each traffic zone.
Preferably, the step S2 includes: and determining candidate positions of the charging stations according to road network planning, power grid planning, land planning, charging requirements and service radius in the target land area.
Preferably, the multi-objective charging station site selection model in step S3 specifically includes a first objective function, a second objective function, and a third objective function; the first objective function is aimed at minimizing the distance from all users to the charging station; the second objective function is aimed at minimizing queuing time of all users; the third objective function is aimed at minimizing charging station construction costs.
Preferably, the first objective function is specifically:
minZ 1 ＝Σ i∈I Σ j∈J h i d ij x ij y j ； (1)
wherein Z is 1 Representing distances of all users to the charging station; h is a i Representing the number of users with charging requirements at the requirement point i; d, d ij Representing the distance from the demand point i to the charging pile candidate point j; x is x ij Poisson distribution probability of the arrival rate of the electric car of the charging pile is represented; y is j A negative exponential distribution probability representing a charging time; i is the total number of the demand points; j is the total number of candidate points of the charging pile.
Preferably, the second objective function is specifically:
minZ 2 ＝Σ i∈I Σ j∈J h i d ij x ij y j T jm ； (3)
wherein Z is 2 Queuing wait time for all users; h is a i Representing the number of users with charging requirements at the requirement point i; d, d ij Representing the distance from the demand point i to the charging pile candidate point j; x is x ij Poisson distribution probability of the arrival rate of the electric car of the charging pile is represented; y is j A negative exponential distribution probability representing a charging time; i is the total number of the demand points; j is the total number of candidate points of the charging pile; t (T) jm The sum of queuing waiting time for m users in charging pile j.
Preferably, the third objective function is specifically:
wherein X is 3 Representing the construction cost of the charging station S j The number of the charging piles at the charging pile candidate points j; f (S) j ) To build S at the selected point j j A station building cost function when the piles are charged; m is the depreciation age of the charging pile, r o The discount rate.
Preferably, the site-building cost function f (S j ) The method comprises the following steps:
wherein S is j The number of the charging piles at the charging pile candidate point j.
A multi-target addressing system of a charging station comprises a charging demand prediction module, a charging station candidate position selection module and a charging station addressing module; the charging demand prediction module and the charging station candidate position selection module are respectively connected with the charging station address selection module, the charging demand prediction module is used for predicting the charging demand of the electric vehicle in the target area and inputting a prediction result into the charging station address selection module, the charging station candidate position selection module is used for preliminarily determining the candidate position of the charging station and inputting determined position information into the charging station address selection module, and the charging station address selection module is used for establishing a plurality of objective functions and acquiring the positions of each candidate point of the charging pile by applying the address selection method.
The beneficial effects of the application are as follows: the application provides a multi-target site selection method of a charging station, which is used for constructing a charging station planning social total cost optimal model based on factors such as the development scale and travel distribution of an electric vehicle of a user.
The system is convenient to calculate, and the planning speed of the charging pile of the electric automobile can be rapidly improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 is a flow chart of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As shown in fig. 1, the embodiment of the application provides a multi-target addressing method of a charging station, which comprises the following steps:
step S1, predicting the charging requirement of an electric automobile in a target area; the method specifically comprises the following steps: dividing traffic locations according to travel rules of the target areas, predicting development scale and travel distribution of electric vehicles in each traffic location, and accordingly predicting charging requirements of the electric vehicles in each traffic location, and further obtaining charging requirements of the electric vehicles of the target areas; and the electric automobile charging requirement of the target area is the sum of the electric automobile charging requirements in each traffic zone.
Step S2, a candidate position of a charging station is preliminarily determined; comprising the following steps: and determining candidate positions of the charging stations according to road network planning, power grid planning, land planning, charging requirements and service radius in the target land area. And S3, establishing a multi-target charging station site selection model by combining the charging requirements of the electric vehicle in the target area and the candidate positions of the charging stations, and carrying out simulation on the multi-target charging station site selection model by using a queuing theory to acquire the positions of each candidate point of the charging piles.
The multi-target charging station site selection model specifically comprises a first target function, a second target function and a third target function; the first objective function is aimed at minimizing the distance from all users to the charging station; the second objective function is aimed at minimizing queuing time of all users; the third objective function is aimed at minimizing charging station construction costs.
The first objective function specifically comprises:
minZ 1 ＝Σ i∈I Σ j∈J h i d ij x ij y j ； (1)
wherein Z is 1 Representing distances of all users to the charging station; h is a i Representing the number of users with charging requirements at the requirement point i; d, d ij Representing the distance from the demand point i to the charging pile candidate point j; x is x ij Poisson distribution probability of the arrival rate of the electric car of the charging pile is represented; y is j A negative exponential distribution probability representing a charging time; i is the total number of the demand points; j is the total number of candidate points of the charging pile.
The second objective function specifically comprises:
minZ 2 ＝∑ i∈I ∑ j∈J h j d ij x ij y j T jm ； (3)
wherein Z is 2 Queuing wait time for all users; h is a i Representing the number of users with charging requirements at the requirement point i; d, d ij Representing the distance from the demand point i to the charging pile candidate point j; x is x ij Poisson distribution probability of the arrival rate of the electric car of the charging pile is represented; y is j A negative exponential distribution probability representing a charging time; i is the total number of the demand points; j is the total number of candidate points of the charging pile; t (T) jm The sum of queuing waiting time for m users in charging pile j.
The third objective function is specifically:
wherein Z is 3 Representing the construction cost of the charging station S j The number of the charging piles at the charging pile candidate points j; f (S) j ) To build S at the selected point j j A station building cost function when the piles are charged; m is the depreciation age of the charging pile, r o The discount rate. Station building cost function f (S j ) The method comprises the following steps:
wherein S is j The number of the charging piles at the charging pile candidate point j.
The following assumptions are used to build the multi-objective charging station site selection model:
1) The destination point is a small area where all users reside. The requirement refers to the number of all electric vehicles in the area that need to be charged.
2) The arrival rate of each charging pile user is distributed according to the regulations, and the residual power is subjected to normal distribution when the electric automobile arrives at the charging pile.
3) It is assumed that the construction grades of the piles are substantially the same.
4) The type of the electric automobile is basically the same as the use specification of the battery.
5) The road condition does not influence the normal running speed of the electric automobile on the road surface, and the speed is constant. The power consumption of the electric automobile in running and the running distance are in a linear relation.
6) No particular preference or need exists for the vehicle owner during use.
From the multi-objective charging station site selection model, it is known that when the distance between the charging demand point of the user and the charging post is kept to a minimum, the waiting time required by the user is also relatively minimum, and in this case, the satisfaction degree exhibited by the user is relatively highest. The satisfaction degree of the user depends on the time spent on charging, and in order to shorten the charging time, more charging piles need to be built, and charging piles with different charging forms also need to be built. In the face of the increasing charging users, the planning range and the number form of the charging posts need to be adjusted according to the actual situation so as to better adapt to the charging environment of the electric automobile which is continuously changed.
Therefore, the application builds the optimal model of the total social cost of charging station planning by using factors such as the development scale, travel distribution and the like of the electric automobile, can improve the planning level of the charging pile of the electric automobile, reduce the comprehensive cost of planning and selecting the site of the charging pile, and reduce the waiting time of the user and the construction investment cost of the charging station under the condition of meeting the charging requirement of the user.
The embodiment of the application also provides a charging station multi-target site selection system which comprises a charging demand prediction module, a charging station candidate position selection module and a charging station site selection module; the charging demand prediction module and the charging station candidate position selection module are respectively connected with the charging station address selection module, the charging demand prediction module is used for predicting the charging demand of the electric vehicle in the target area and inputting a prediction result into the charging station address selection module, the charging station candidate position selection module is used for preliminarily determining the candidate position of the charging station and inputting determined position information into the charging station address selection module, and the charging station address selection module is used for establishing a plurality of objective functions and acquiring the positions of each candidate point of the charging pile by applying the address selection method.
The system can be used for rapidly improving the planning speed of the charging pile of the electric automobile.
Those of ordinary skill in the art will appreciate that the elements of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the elements of the examples have been described generally in terms of functionality in the foregoing description to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the division of the units is merely a logic function division, and there may be other division manners in actual implementation, for example, multiple units may be combined into one unit, one unit may be split into multiple units, or some features may be omitted.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application, and are intended to be included within the scope of the appended claims and description.
Claims (9)
1. The multi-target site selection method for the charging station is characterized by comprising the following steps of:
step S1, predicting the charging requirement of an electric automobile in a target area;
step S2, a candidate position of a charging station is preliminarily determined;
and S3, establishing a multi-target charging station site selection model by combining the charging requirements of the electric vehicle in the target area and the candidate positions of the charging stations, and carrying out simulation on the multi-target charging station site selection model by using a queuing theory to acquire the positions of each candidate point of the charging piles.
2. The multi-target location method of charging station according to claim 1, wherein the step S1 specifically comprises: dividing traffic locations according to travel rules of the target areas, predicting development scale and travel distribution of electric vehicles in each traffic location, and accordingly predicting charging requirements of the electric vehicles in each traffic location, and further obtaining charging requirements of the electric vehicles of the target areas; and the electric automobile charging requirement of the target area is the sum of the electric automobile charging requirements in each traffic zone.
3. The multi-target location method of charging station according to claim 1, wherein the step S2 comprises: and determining candidate positions of the charging stations according to road network planning, power grid planning, land planning, charging requirements and service radius in the target land area.
4. The multi-objective charging station addressing method according to claim 1, wherein the multi-objective charging station addressing model in step S3 specifically includes a first objective function, a second objective function, and a third objective function; the first objective function is aimed at minimizing the distance from all users to the charging station; the second objective function is aimed at minimizing queuing time of all users; the third objective function is aimed at minimizing charging station construction costs.
5. The multi-objective addressing method of charging station according to claim 4, wherein the first objective function (sum of distances from all users to charging station is minimum) is specifically:
wherein Z is 1 Representing distances of all users to the charging station; h is a i Representing the number of users with charging requirements at the requirement point i; d, d ij Representing the distance from the demand point i to the charging pile candidate point j; x is x ij Poisson distribution probability of the arrival rate of the electric car of the charging pile is represented; y is j A negative exponential distribution probability representing a charging time; i is the total number of the demand points; j is the total number of candidate points of the charging pile.
6. The multi-objective addressing method of charging station according to claim 4, wherein the second objective function (minimum sum of queuing waiting time) is specifically:
wherein Z is 2 Queuing wait time for all users; h is a i Representing the number of users with charging requirements at the requirement point i; d, d ij Representing the distance from the demand point i to the charging pile candidate point j; x is x ij Poisson distribution probability of the arrival rate of the electric car of the charging pile is represented; y is j A negative exponential distribution probability representing a charging time; i is the total number of the demand points; j is the total number of candidate points of the charging pile; t (T) jm The sum of queuing waiting time for m users in charging pile j.
7. The charging station multi-objective addressing method of claim 4, wherein the third objective function (charging station construction cost minimization) is specifically:
wherein Z is 3 Representing the construction cost of the charging station S j The number of the charging piles at the charging pile candidate points j; f (S) j ) To build S at the selected point j j A station building cost function when the piles are charged; m is the depreciation age of the charging pile, r o The discount rate.
8. A according to claim 7A multi-target addressing method of a charging station, characterized in that the station building cost function f (S j ) The method comprises the following steps:
wherein S is j The number of the charging piles at the charging pile candidate point j.
9. The charging station multi-target site selection system is characterized by comprising a charging demand prediction module, a charging station candidate position selection module and a charging station site selection module; the charging demand prediction module and the charging station candidate position selection module are respectively connected with the charging station address selection module, the charging demand prediction module is used for predicting the charging demand of the electric vehicle in the target area and inputting a prediction result into the charging station address selection module, the charging station candidate position selection module is used for preliminarily determining the candidate position of the charging station and inputting determined position information into the charging station address selection module, and the charging station address selection module is used for establishing a plurality of objective functions and acquiring the positions of each candidate point of the charging pile by applying the address selection method of any one of claims 1-8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117077985A (en) * | 2023-10-16 | 2023-11-17 | 浙江优能电力设计有限公司 | Electric vehicle charging pile planning method and system according to charging requirements |
| CN117557069A (en) * | 2024-01-10 | 2024-02-13 | 长峡数字能源科技(湖北)有限公司 | Charging pile site selection method and system |
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2023
- 2023-02-23 CN CN202310160556.6A patent/CN116822685A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117077985A (en) * | 2023-10-16 | 2023-11-17 | 浙江优能电力设计有限公司 | Electric vehicle charging pile planning method and system according to charging requirements |
| CN117077985B (en) * | 2023-10-16 | 2024-04-09 | 浙江优能电力设计有限公司 | Electric vehicle charging pile planning method and system according to charging requirements |
| CN117557069A (en) * | 2024-01-10 | 2024-02-13 | 长峡数字能源科技(湖北)有限公司 | Charging pile site selection method and system |
| CN117557069B (en) * | 2024-01-10 | 2024-03-26 | 长峡数字能源科技(湖北)有限公司 | Charging pile address selection method and system, electronic equipment and storage medium |
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